Simplify vastu-slam configuration and increase default grid size
- Rename header from VastuMap to VastuSLAM - Increase initial grid from 300x300 to 800x800 cells (20m at 2.5cm) - Move log_odds config from grid to slam section - Consolidate pose_graph under slam section - Remove unused sections: submap, motion_filter, query, pathfinding, exploration - Adjust correlative matcher defaults for better performance
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codetiger
+57
-164
@@ -1,5 +1,4 @@
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# VastuMap Occupancy Grid Configuration
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# Inspired by Google Cartographer SLAM parameters
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# VastuSLAM Configuration
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# Units: meters (m), radians (rad), cells (n)
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# ============================================================================
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@@ -7,41 +6,22 @@
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# ============================================================================
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grid:
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# Cell resolution in meters per cell
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resolution: 0.025 # 2.5cm cells (Cartographer default: 0.05)
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resolution: 0.025 # 2.5cm cells
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# Initial grid dimensions (cells)
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initial_width: 300
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initial_height: 300
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initial_width: 800 # 20m at 2.5cm
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initial_height: 800 # 20m at 2.5cm
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# Grid origin mode: "center" or "corner"
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origin_mode: center
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# Custom origin offset (only used if origin_mode: custom)
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# Custom origin offset (only used if origin_mode: corner)
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origin_x: 0.0
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origin_y: 0.0
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# Enable automatic grid expansion
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auto_expand: true
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# Log-odds occupancy model (Cartographer-style)
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# Lower values = more observations needed to establish walls
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log_odds:
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# Log-odds increment for hit (lidar endpoint)
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# Cartographer: ~20 (P=0.55), VastuMap aggressive: 70 (P=0.67)
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l_hit: 20 # Cartographer default: ~3-5 observations for wall
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# Log-odds decrement for miss (ray passes through)
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# Cartographer: ~-4 (P=0.49), VastuMap aggressive: -28 (P=0.43)
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l_miss: -4 # Cartographer default: asymmetric for sticky walls
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# Cell classification thresholds
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l_occupied_threshold: 50
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l_free_threshold: -50
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# Clamping range (probability bounds)
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l_min: -200 # P ≈ 0.12
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l_max: 200 # P ≈ 0.88
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# ============================================================================
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# Sensor Configuration
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# ============================================================================
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@@ -76,18 +56,36 @@ sensor:
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# SLAM Configuration
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# ============================================================================
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slam:
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# Correlative scan matcher (local SLAM)
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# Log-odds occupancy model (Cartographer-style)
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log_odds:
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# Log-odds increment for hit (lidar endpoint)
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# Cartographer default: ~20 (P=0.55)
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l_hit: 20
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# Log-odds decrement for miss (ray passes through)
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# Asymmetric to make walls "stickier" than free space
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l_miss: -4
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# Cell classification thresholds
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l_occupied_threshold: 50
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l_free_threshold: -50
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# Clamping range (probability bounds)
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l_min: -200 # P ~ 0.12
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l_max: 200 # P ~ 0.88
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# Correlative scan matcher
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correlative:
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enabled: true
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# Search window
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search_x: 0.3 # meters
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search_y: 0.3 # meters
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search_theta: 0.15 # radians (~8.6°)
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search_theta: 0.15 # radians (~8.6 degrees)
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# Search resolution (finer = more accurate but slower)
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linear_resolution: 0.01 # Was 0.02, now 1cm
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angular_resolution: 0.01 # Was 0.02, now ~0.57°
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# Search resolution
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linear_resolution: 0.02 # 2cm
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angular_resolution: 0.02 # ~1.1 degrees
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# Multi-resolution search (coarse-to-fine)
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multi_resolution: true
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@@ -95,7 +93,7 @@ slam:
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# Gaussian scoring (uses distance field)
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use_gaussian_scoring: true
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gaussian_sigma: 0.05 # Was 0.10, now 2x grid resolution
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gaussian_sigma: 0.10 # 10cm - matches 2x grid resolution
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# Gauss-Newton refinement for sub-pixel accuracy
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gn_max_iterations: 10
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@@ -110,158 +108,53 @@ slam:
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# Maximum points to use (0 = all)
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max_points: 0
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# Optional: skip correlative search for reliable odometry
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skip_when_confident: false
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skip_confidence_threshold: 0.8
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# Branch-and-bound matcher (for loop closure)
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# More efficient than brute-force for large search windows
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branch_bound:
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enabled: true
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# Large search window for loop closure
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search_x: 2.0 # meters (Cartographer global: ~7m)
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search_y: 2.0 # meters
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search_theta: 0.5 # radians (~28°)
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# Angular resolution
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angular_resolution: 0.02 # ~1.1°
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# Minimum score ratio (fraction of max possible)
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min_score_ratio: 0.5
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# Loop closure detection
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loop_closure:
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enabled: true
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# Keyframe creation thresholds
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keyframe_distance: 0.3 # Was 0.5, now denser
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keyframe_rotation: 0.35 # Was 0.5 (~20° now)
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keyframe_distance: 0.5 # 50cm between keyframes
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keyframe_rotation: 0.5 # ~28 degrees
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# LiDAR-IRIS descriptor matching
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max_descriptor_distance: 80 # Was 100, now stricter (out of 640)
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max_descriptor_distance: 100 # Out of 640 bits
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# Spatial constraints
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min_loop_gap: 20 # Minimum pose index gap
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max_loop_distance: 10.0 # Maximum spatial distance (m)
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max_loop_distance: 5.0 # Maximum spatial distance (m)
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# Verification
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# Verification via scan matching
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min_verification_score: 0.6
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use_branch_bound: true # Use B&B instead of correlative
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use_branch_bound: true # Use branch-and-bound instead of correlative
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# Verification search window (fallback if B&B disabled)
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verification_search_window: 0.2
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verification_angular_window: 0.2
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# Verification search window
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verification_search_window: 0.2 # 20cm
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verification_angular_window: 0.2 # ~11 degrees
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# ============================================================================
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# Submap Configuration
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# ============================================================================
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submap:
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scans_per_submap: 50 # Cartographer: 90
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overlap_scans: 10 # Smooth transitions between submaps
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local_grid_size: 200 # 5m × 5m at 2.5cm resolution
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max_distance_per_submap: 3.0 # Force new submap after this distance
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max_submaps: 100 # Maximum submaps to store
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# Pose graph optimization
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pose_graph:
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max_iterations: 20
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convergence_threshold: 1e-4
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damping: 1e-3
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# Loop closure between submaps
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min_loop_closure_gap: 3 # Minimum submap index gap
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max_loop_closure_distance: 10.0
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# Edge weights
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odometry_weight: 100.0 # Strong trust in encoder odometry
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loop_weight: 50.0 # Lower to handle noise
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# ============================================================================
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# Pose Graph Optimization
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# ============================================================================
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pose_graph:
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# Optimization trigger
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optimize_every_n_nodes: 0 # 0 = only on loop closure
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# Huber loss for robust optimization (downweight outliers)
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huber_scale: 0.1 # 10cm
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# Solver parameters
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max_iterations: 50
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convergence_threshold: 1e-5
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# Background optimizer
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background_optimizer:
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max_iterations: 50
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convergence_threshold: 1e-5
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huber_scale: 0.1
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# Huber loss for robust optimization (downweight outliers)
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huber_scale: 0.1 # 10cm
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# Trigger optimization every N nodes (0 = only on loop closure)
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trigger_every_n_nodes: 0
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# Edge weights
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odometry_weight: 100.0 # Strong trust in encoder odometry
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loop_closure_weight: 50.0 # Lower to handle noise
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# Background thread (optional, not recommended for ARM)
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use_background_thread: false
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# ============================================================================
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# Motion Filter Configuration (Cartographer-style)
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# ============================================================================
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# Gates scan processing based on robot movement to reduce redundant observations
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# and save CPU when stationary. A scan is processed if ANY threshold is exceeded.
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motion_filter:
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# Enable/disable motion filtering (true = filter enabled)
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enabled: true
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# Maximum time between scan insertions (seconds)
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# Ensures at least some updates even when stationary
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max_time_secs: 5.0
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# Maximum distance before forcing scan insertion (meters)
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max_distance: 0.2
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# Maximum rotation before forcing scan insertion (radians)
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# 0.17 rad ≈ 10 degrees
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max_angle: 0.17
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# ============================================================================
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# Query Configuration
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# ============================================================================
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query:
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# Frontier detection
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frontier:
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min_frontier_size: 5 # Minimum cells for valid frontier
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use_8_connectivity: true # Use 8-connected grid (vs 4-connected)
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# Traversability checking
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traversability:
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safety_margin: 0.05 # Extra clearance around robot (m)
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# ============================================================================
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# Pathfinding Configuration
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# ============================================================================
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pathfinding:
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# A* algorithm settings
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astar:
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allow_diagonal: true # Enable 8-directional movement
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diagonal_cost: 1.414 # sqrt(2) cost for diagonal moves
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max_iterations: 100000 # Max nodes to expand
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clearance_weight: 0.0 # Penalty for paths near obstacles (0=disabled)
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# Path smoothing
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smoothing:
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enabled: true
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los_step_size: 0.05 # Line-of-sight check step (m)
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max_iterations: 100 # Midpoint smoothing iterations
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# ============================================================================
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# Exploration Configuration
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# ============================================================================
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exploration:
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# Frontier selection
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min_frontier_size: 5
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frontier_reached_threshold: 0.3 # Distance to consider frontier reached (m)
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waypoint_threshold: 0.1 # Distance for waypoint reached (m)
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# Planning
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max_plan_distance: 5.0 # Maximum planning distance (m)
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replan_interval: 1.0 # Seconds between replans when blocked
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# Failure handling
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max_consecutive_failures: 5
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# Path preferences
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path_clearance_weight: 0.0
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smooth_paths: true
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# Recovery behavior
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recovery:
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turn_angle: 0.785 # 45 degrees in radians
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backup_distance: 0.1 # Backup distance (m)
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# Background thread (not recommended for ARM)
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use_background_thread: false
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# ============================================================================
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# Map Persistence
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